Bucks vs. Heat breakdown: How did Miami beat the best team in the NBA?

15 min readSep 25, 2020

After last Tuesday’s game 7, all everyone wants to talk about is how the Clippers blew a 3–1 lead and lost to the Nuggets. People seem to forget that these playoffs have been full of some of the most interesting moments, games, and series in recent memory. Every year, I make an entire playoff bracket before the playoffs even start. According to my bracket, I predicted that the Clippers-Nuggets series was going to 7 games (even though I had the Clippers coming out on top). The point is, I always knew that the Nuggets were a very good team, and I am not very surprised by the outcome (although it is somewhat surprising that the Nuggets fell into a 3–1 hole and then were able to come back for the second time in the playoffs). However, my Bucks-Heat series prediction was blatantly wrong (I predicted the Bucks to win in 6 games). In my defense, I don’t think anyone saw this coming. This is why I want to focus on figuring out what happened, both from a statistical and a visual perspective.

Statistics

Team Statistics

From the getgo, we can just look at some of the major statistical categories and figure out that Milwaukee fell way short of expectations. Below, I have listed some of the most important statistical categories that I found to be very telling of why Milwaukee lost the series. The statistics are given for both the Bucks and the Heat during the regular season, the first round of the playoffs, and the second round of the playoffs (this is when the teams faced each other). Explanations of the abbreviations and the definitions of each of the statistics are given below. Each row also has two (or three, depending on if two cells in the row have the same value) highlighted cells for each team: one red and one green. The green represents the best value that the team was able to achieve for that statistic when comparing the regular season, the first round of the playoffs, and the second round of the playoffs, while the red represents the worst value.

Here is a brief explanation of each of these statistics:

TS%: “True Shooting Percentage.” An adjustment to shooting percentage that accounts for free-throws and three-point shots given by the formula in this link.
3PM: “3 Point Makes.” The average number of three-pointers made per game.
3P%: “Three-Point Percentage.” The number of three-point makes divided by the number of three-point attempts per game.
FTA: “Free-Throw Attempts.” The average number of free-throws attempted per game.
FT%: “Free-Throw Percentage.” The average number of free-throw makes divided by the average number of free-throw attempts per game.
ORB%: “Offensive Rebound Percentage.” The percentage of available rebounds (while a team is on offense) that a team grabs (gives the team an extra possession).
DRB%: “Defensive Rebound Percentage.” The percentage of available rebounds (while a team is on defense) that a team grabs to regain possession of the ball. If team A and team B are playing against each other, team A’s DRB% = 1-ORB% of team B and team A’s ORB% = 1-DRB% of team B
TRB%: “Total Rebound Percentage.” The percentage of total available rebounds that a team grabs in a game.
AST%: The percentage of a team’s field goals that were assisted on.
TOV%: The percentage of a team’s possessions that resulted in a turnover.
ORTG: “Offensive Rating.” A team’s average number of points scored per 100 possessions.
DRTG: “Defensive Rating.” A team’s average number of points allowed by the opposing team per 100 possessions.
PTS off TO: “Points off Turnovers.” The average number of points scored off the opposing team’s turnovers per game.
2nd PTS: “Second-chance Points.” The average number of points scored off of second chance possessions (a result of offensive rebounds) per game.
FBPS: “Fast-break Points.” The average number of points scored from fast breaks per game.
PITP: “Points in the Paint.” The average number of points scored in the paint area per game.
Opp PTS off TO, Opp 2nd PTS, Opp FBPS, Opp PITP: Same as above except these represent the averages of the opposing team.
Pace: The average number of possessions a team has per game.

Right off the bat, the sea of red for the Bucks jumps out in the vs. MIA column. This shows that the Bucks had some of their worst performances in major statistical categories in that series. On the Miami side, this column is instead filled with green, showing that this series brought out some of the best performances from the Heat team in many of these statistical categories. When also looking at the series against the Indiana Pacers, the Heat seemed to make a huge jump in the playoffs, improving in many statistical categories when compared to the regular season. Looking at the statistics, it is quite obvious that the Bucks underachieved in many categories, which obviously made it a lot tougher to beat a strong Heat team. Although many of these statistics can individually describe where the Bucks fell short, there does seem to be a general trend, which centers around the pace and the speed of the game.

Looking at the pace of the Bucks in the regular season versus in the playoffs, there is a sharp drop off. The Bucks actually had the fastest-paced offense in the league in the regular season. In general, the pace of the playoffs is likely to be a lot slower, with teams using more of the shot clock on each possession while trying to look for good, efficient shots. For example, the average pace of NBA teams this year dropped from 100.3 in the regular season to 97.9 in the playoffs. This slower pace really stagnated the Bucks offense as they tried to navigate a style of play that they weren’t used to. Another statistic that showcases this the fastbreak points (FBPS), which also had a sharp drop off in the playoffs. AST% also shows that Milwaukee’s offense stalled in the Miami series, hitting its lowest level. The only things that the Bucks did well in the series against Miami were getting to the free-throw line (and even then they were somewhat inconsistent with their free-throws) and not turning the ball over.

On the other hand, the Heat were very prepared for playoff basketball. They don’t show as much of a drop in pace (they had the 4th slowest average pace during the regular season) and they were able to have their best Opp FBPS value during the series against the Bucks, showing that they really clamped down to try to disrupt Milwaukee’s natural style of offensive play. The Heat also finished the regular season third in AST%, but they were still able to improve in this area in the series against Milwaukee. Overall, the Heat offense flowed really well. No wonder Brad Stevens compared them to the prime Warriors offenses we have seen over the last few years.

Player Statistics

In order to measure player impact, we must look at individual statistics to determine how players performed in the regular season versus in the playoffs. It is really hard to look at a single stat to describe the totality of a player’s game, but we can attempt to do so using a stat called game score. Game score takes the major box score categories into account and outputs a single number that represents player productivity. It is defined by the formula given here. Some of the best game scores of all time are in the 50+ range (for example, Kobe Bryant’s 81 point game had a game score of 63.5 and Devin Booker’s 70 point game had a game score of 54.5), but >40 is still considered an incredible game. Roughly average on the game score scale is slightly below 10.

One glaring omission from game score statistic is minutes played. This is why game score isn’t super useful to evaluate players who are super deep in the rotation of a team (these are players whose minutes are usually cut in the playoffs). Also, game score doesn’t account for advanced statistics and overall team statistics, so it is a really rough estimate of how productive a player is in a particular game. Below, I have listed the average game scores for each of the players on the Heat and the Bucks in the regular season, the first round of the playoffs, and in the second round of the playoffs. The color-coding scheme is the same as it was for the team statistics table.

It is pretty obvious that most of the Bucks players took a step back in the playoffs when comparing their performance to that of the regular season. However, most of the Heat players actually improved their level of play. It’s especially important to note that Jimmy Butler and Bam Adebayo, probably the Heat’s two most important players (and their two all-stars this season), were able to have their best average game scores in the series versus Milwaukee. On the other hand, the NBA MVP, Giannis Antetokounmpo, had some of his worst performances according to game score in the series against Miami. Although key Bucks players like Khris Middleton and Brook Lopez were able to improve from the Orlando series, the rest of the main rotation players fell off.

Game Film Breakdown

After rewatching the games, I found many strategies that Miami implemented on both the offensive and defensive ends to make winning plays.

Game 1

The Heat tended to attack the paint when Giannis Antetokounmpo and Brook Lopez went to the bench (when the Bucks were playing a smaller lineup), especially when Giannis wasn’t in.
Jimmy Butler did a lot of the attacking of the paint.
There were many high screens by Bam Adebayo to set up three-pointers or drives (a lot of the drives came from Goran Dragic).
The Bucks let Butler dribble too much and get to his spots in the midrange. Obviously, man defense wasn’t working.
Miami’s shooters created A LOT of spacing. The paint was almost never clogged. This drew Giannis out of the paint, especially when he was guarding wings (e.g. Jae Crowder, Andre Iguodala). See the picture below for a visual on the amount of space in the paint Milwaukee left open in game 1.

Brook Lopez was also drawn out of the paint by Kelly Olynyk.
Even Adebayo tended to stay further out from the painted area.
The Heat really outplayed the Bucks at the free-throw line (Giannis seriously needs to improve).

Game 2

The Heat still had Bam Adebayo set some more high screens.
However, I also saw some high screens from Jimmy Butler (mostly for Goran Dragic).
Adebayo didn’t set screens that led to threes (there were plenty of these in game 1) but rather more screens that led to drives.
Again, Kelly Olynyk really drew his defender out to the three.
For example, there was a play where Giannis stayed to help in the paint and Olynyk made him pay by hitting a three.
Milwaukee was pretty bad shooting the three. They didn’t get many great looks.
Again, a lot of offense was created by Khris Middleton off the dribble (the prime example of this is when he was expected to take the last shot to tie the game).
Brook Lopez was getting attacked in the middle a lot more in this game. Giannis was definitely the best interior defender.

Game 3

There were a lot of drives by Tyler Herro which created offense.
Giannis was settling for three contested pointers (he didn’t make a single one this game).
Milwaukee’s role players were doing better at getting open threes.
George Hill was a lot better about attacking the paint.
Giannis had a lot better paint presence (unlike in game 1). He wasn’t drawn out so often by the Miami wing players.
In my opinion, this game was one that the Bucks should have won.

Game 4

Giannis was really good about attacking the basket as he normally did in the regular season (before he got injured, of course). The Heat were letting him get to the basket and not effectively denying him the ball.
Duncan Robinson was hitting a lot of catch and shoot threes (or threes with one dribble coming off a pass).
Bam Adebayo was pressuring the Bucks defense with his mid-range jumper.
Brook Lopez was a lot better about protecting the paint.
Khris Middleton had some overtime heroics.

Game 5

The Bucks were better about keeping the Heat off the three-point line.
This time, Donte DiVencenzo was making it tough for Duncan Robinson to get good three-point looks.
The red-hot Jae Crowder also got slightly fewer looks, even though the Bucks did give up some open threes to him every once in a while.
Tyler Herro had some really good passing in this game. He was able to find open teammates really well. His setups were mostly for teammates in the paint (or cutting to the basket) rather than at the three.

Generally

Mostly, the paint was left open because of Miami’s spacing and three-point marksmen, so savvy players like Jimmy Butler could obviously exploit this (this was especially apparent in game 1).
Later on, the Bucks did a better job of limiting looks from the paint, but that’s where Heat players like Tyler Herro, Duncan Robinson, and Jae Crowder took advantage.
When the paint was clogged, the Heat settled for quality mid-range jumpers, floaters (mostly by Goran Dragic), or set up a play to get a three-point attempt. Even guys like Bam Adebayo were not afraid to shoot when given the space.
There was also a lot of cutting to the paint when Giannis wasn’t in, which led to easy dimes or floaters for the ball handling player.
In the earlier games of the series, Giannis created offense when he caught the ball in the low or high post by kicking out to a shooter. Later on in the series, Giannis attacked the rim better, playing a style that he was more used to in the regular season.
Brook Lopez got attacked in the paint quite a bit, and it was a mostly successful effort from the Heat.
Khris Middleton was expected to bail out the possession quite a few times when the Bucks weren’t finding offense elsewhere.
For the Heat, Jimmy Butler took on the bail out role, but didn’t have to do it as often because of the Heat’s good ball movement.
Goran Dragic created a lot of offense off the dribble, especially when assisted by an Adebayo screen. The Bucks mostly let him go to his strong hand (left), which let him get to the rim, get a floater, or get a look at an open three-pointer.
The Heat players just seemed to blow by the Bucks defenders too easily at times.
Improvements for the Bucks: In some moments of the series (e.g. game 1), the X factor was obviously Jimmy Butler. I think Milwaukee could have done a better job of denying him the ball, and if not quickly getting the ball out of his hands with a double team once he started hitting shots. Butler is a decent passer but not an elite playmaker so this could have been effective. I also think the Bucks could have implemented a zone defense from time to time, which would have helped to contain the three-point shooters. On offense, I think the Bucks expected way too much from Middleton. It would have been better to attack Adebayo early in games and try to get him in foul trouble, which would have opened things up for Giannis to play the way he normally does. Also, although the rookie Kendrick Nunn didn’t play as much, I did see that from time to time the Bucks players found it really easy to attack him or get past him on defense, so I think they could have done a better job of getting a favorable switch onto him.
Overall, hats off to the Heat for an amazing series.

Is there any way this could have been predicted?

The reason I’m even writing this article is that at the beginning of the playoffs, almost no one had expected that the Bucks would lose in the second round in only five games. After a convincing 4–0 sweep of the Pacers in the first round, there were definitely some changes of opinion about the outcome of the Bucks-Heat series, although the Bucks were definitely still the favorites to win. The odds for the Bucks winning were set at -400 (compared to +300 for Miami) before the series, and they were still favored to win the series even after losing game 1. It’s easy to look back at what happened in the series to figure out why the Bucks lost, but it’s really hard to make the prediction in the first place.

From a statistical standpoint, I wasn’t able to come up with a clear reason why almost no one was able to predict the Heat beating the Bucks. First of all, I tried to see if the Bucks actually performed well versus teams that were good during the regular season. I tried looking at Milwaukee’s regular season win-loss record against >0.500 teams (teams that have won more than 50% of their games). Between Milwaukee and Miami, there seemed to be no difference here: the Bucks were 17–13 while the Heat were 17–14. To summarize some of the other elite teams, the Los Angeles Lakers were 20–14, the Los Angeles Clippers were 20–12, the Toronto Raptors were 16–15, the Boston Celtics were 15–15, and the Denver Nuggets were 17–15. This shows that the Bucks were on par record-wise with some of the best teams in the NBA when it comes to winning record against good teams, meaning that this probably wouldn’t make for a good predictor of playoff success.

I also analyzed what I perceived to be the strength of the Heat: ball movement (finished 6th in the league for average assists per game during the regular season: tied with the Bucks) and three-point shooting (finished 2nd in the league in three-point percentage during the regular season). To use this information, I calculated the winning percentages for each NBA team when their opponent shot better than 35.8% from three (this was the NBA average three-point percentage this season) and had more than 24.4 (≥25) assists (NBA average number of assists per game for teams this season). The results are below.

There doesn’t seem to be much of a relationship between team success and how the team fared against perimeter teams that move the ball well, although the Bucks definitely had a significant drop in winning percentage in these games.

The third and final statistic that I looked at was how the Bucks played in slower-paced games. First of all, I filtered for games in which the Bucks had a slower pace than their season average (105.1 according to basketball-reference: calculated slightly differently from NBA.com). Their record in these games was 32–10, which is just about on par with their regular season winning percentage, which was 76.7%. Afterward, I filtered for games in which the Bucks played slower than the NBA overall average for pace this season (100.3 according to basketball-reference). In these games, they were 8–3, which is slightly worse than their regular season win record. Another analysis I did was filtering for games in which the Bucks played against >0.500 teams at a slower pace than their season average. In these games, they were 11–8. Finally, I filtered for games in which the Bucks played against >0.500 teams at a slower pace than the NBA average pace. In these games, they were 2–2. From this, we can see that the Bucks played slightly worse in the regular season when playing at a slower pace, but it’s nothing drastic and the sample size is really small.

One big caveat is that I’m looking at these statistics in hindsight. I saw what went wrong for Milwaukee and right for the Heat in the series and tried to analyze these things during the regular season. There don’t seem to be many correlations in the first place, and I would definitely not extrapolate this as a predictor of a team’s playoff success in future NBA seasons. It was some good food for thought though.

Conclusion

Upsets are not a new phenomenon in the NBA, but this is one of the biggest ones in my recent memory. Overall, it’s easy to see that the Heat just simply stepped up their game in the playoffs, and the Bucks never looked the same as soon as they got to the NBA “bubble” that has hosted the end of the season and the playoffs. Apart from the statistics and style of play, we can probably also point to intangible factors to explain this result, such as Jimmy Butler’s competitive fire and leadership, or the young Heat players’ desire to make a name for themselves in the NBA. The bubble also probably changed the dynamics of how games turn out (e.g. Milwaukee might have done better with their home crowd backing them). Taking all these things into account, I guess the moral of the story is: don’t beat yourself up over getting this prediction wrong. *insert Charles Barkley “guarantee” meme*